Martin Wistuba

Publication Activity (10 Years)

Years Active: 2011-2023
Publications (10 Years): 67

Top Venues

CoRR

ECML/PKDD (2)

DSAA

NeurIPS

University of Hildesheim, Germany (PHD)

Education History

University of Hildesheim, Germany

PHD

Publications

Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov
Variational Boosted Soft Trees. CoRR (2023)
Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella
Renate: A Library for Real-World Continual Learning. CoRR (2023)
Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
Scaling Laws for Hyperparameter Optimization. NeurIPS (2023)
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella
Continual Learning with Low Rank Adaptation. CoRR (2023)
Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
Deep Power Laws for Hyperparameter Optimization. CoRR (2023)
Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov
Variational Boosted Soft Trees. AISTATS (2023)
Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR (2023)
Martin Wistuba, Arlind Kadra, Josif Grabocka
Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning. CoRR (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
Continual Learning with Transformers for Image Classification. CoRR (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau
Memory Efficient Continual Learning for Neural Text Classification. CoRR (2022)
Martin Wistuba, Arlind Kadra, Josif Grabocka
Supervising the Multi-Fidelity Race of Hyperparameter Configurations. NeurIPS (2022)
Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. AAAI (2022)
David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
Continual Learning with Transformers for Image Classification. CVPR Workshops (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
Memory Efficient Continual Learning with Transformers. NeurIPS (2022)
Arunima Chaudhary, Alayt Issak, Kiran Kate, Yannis Katsis, Abel N. Valente, Dakuo Wang, Alexandre V. Evfimievski, Sairam Gurajada, Ban Kawas, A. Cristiano I. Malossi, Lucian Popa, Tejaswini Pedapati, Horst Samulowitz, Martin Wistuba, Yunyao Li
AutoText: An End-to-End AutoAI Framework for Text. AAAI (2021)
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang
A Comprehensive Survey on Hardware-Aware Neural Architecture Search. CoRR (2021)
Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. NeurIPS Datasets and Benchmarks (2021)
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang
Hardware-Aware Neural Architecture Search: Survey and Taxonomy. IJCAI (2021)
Martin Wistuba, Josif Grabocka
Few-Shot Bayesian Optimization with Deep Kernel Surrogates. CoRR (2021)
Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. CoRR (2021)
Hoang Thanh Lam, Beat Buesser, Hong Min, Tran Ngoc Minh, Martin Wistuba, Udayan Khurana, Gregory Bramble, Theodoros Salonidis, Dakuo Wang, Horst Samulowitz
Automated Data Science for Relational Data. ICDE (2021)
Martin Wistuba, Josif Grabocka
Few-Shot Bayesian Optimization with Deep Kernel Surrogates. ICLR (2021)
Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea
Searching for Machine Learning Pipelines Using a Context-Free Grammar. AAAI (2021)
Martin Wistuba
XferNAS: Transfer Neural Architecture Search. ECML/PKDD (3) (2020)
Djallel Bouneffouf, Charu C. Aggarwal, Thanh Hoang, Udayan Khurana, Horst Samulowitz, Beat Buesser, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray
Survey on Automated End-to-End Data Science? IJCNN (2020)
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
Automation of Deep Learning - Theory and Practice. ICMR (2020)
Martin Wistuba, Tejaswini Pedapati
Learning to Rank Learning Curves. CoRR (2020)
Martin Wistuba, Tejaswini Pedapati
Learning to Rank Learning Curves. ICML (2020)
Martin Wistuba, Ambrish Rawat
Scalable Large Margin Gaussian Process Classification. ECML/PKDD (2) (2019)
Martin Wistuba
XferNAS: Transfer Neural Architecture Search. CoRR (2019)
Charu C. Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray
How can AI Automate End-to-End Data Science? CoRR (2019)
Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba
Optimal Exploitation of Clustering and History Information in Multi-armed Bandit. IJCAI (2019)
Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba
Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit. CoRR (2019)
Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir R. Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati
NeuNetS: An Automated Synthesis Engine for Neural Network Design. CoRR (2019)
Martin Wistuba, Tejaswini Pedapati
Inductive Transfer for Neural Architecture Optimization. CoRR (2019)
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
A Survey on Neural Architecture Search. CoRR (2019)
Hoang Thanh Lam, Tran Ngoc Minh, Mathieu Sinn, Beat Buesser, Martin Wistuba
Learning Features For Relational Data. CoRR (2018)
Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards
Adversarial Robustness Toolbox v0.2.2. CoRR (2018)
Martin Wistuba
Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations. ECML/PKDD (2) (2018)
Martin Wistuba
Practical Deep Learning Architecture Optimization. DSAA (2018)
Tran Ngoc Minh, Mathieu Sinn, Hoang Thanh Lam, Martin Wistuba
Automated Image Data Preprocessing with Deep Reinforcement Learning. CoRR (2018)
Martin Wistuba, Ambrish Rawat
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data. CoRR (2018)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. Mach. Learn. 107 (1) (2018)
Kathrin Bujna, Martin Wistuba
Multi-Plant Photovoltaic Energy Forecasting Challenge with Regression Tree Ensembles and Hourly Average Forecasts. DC@PKDD/ECML (2017)
Hanh T. H. Nguyen, Martin Wistuba, Lars Schmidt-Thieme
Personalized Tag Recommendation for Images Using Deep Transfer Learning. ECML/PKDD (2) (2017)
Martin Wistuba
Finding Competitive Network Architectures Within a Day Using UCT. CoRR (2017)
Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rêgo Drumond, Lars Schmidt-Thieme
Personalized Deep Learning for Tag Recommendation. PAKDD (1) (2017)
Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. CoRR (2017)
Martin Wistuba
Bayesian Optimization Combined with Successive Halving for Neural Network Architecture Optimization. AutoML@PKDD/ECML (2017)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Automatic Frankensteining: Creating Complex Ensembles Autonomously. SDM (2017)
Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
Fast classification of univariate and multivariate time series through shapelet discovery. Knowl. Inf. Syst. 49 (2) (2016)
Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
Learning DTW-Shapelets for Time-Series Classification. CODS (2016)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization. ECML/PKDD (1) (2016)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Hyperparameter Optimization Machines. DSAA (2016)
Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme
Bank Card Usage Prediction Exploiting Geolocation Information. CoRR (2016)
Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts. ECML/PKDD (1) (2016)
Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme
Ultra-Fast Shapelets for Time Series Classification. CoRR (2015)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Learning Data Set Similarities for Hyperparameter Optimization Initializations. MetaSel@PKDD/ECML (2015)
Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. ICTAI (2015)
Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
Hyperparameter Optimization with Factorized Multilayer Perceptrons. ECML/PKDD (2) (2015)
Nghia Duong-Trung, Martin Wistuba, Lucas Rêgo Drumond, Lars Schmidt-Thieme
Geo_ML @ MediaEval Placing Task 2015. MediaEval (2015)
Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. IEEE Trans. Knowl. Data Eng. 27 (6) (2015)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Learning hyperparameter optimization initializations. DSAA (2015)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization. ECML/PKDD (2) (2015)
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Sequential Model-Free Hyperparameter Tuning. ICDM (2015)
Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
Scalable Discovery of Time-Series Shapelets. CoRR (2015)
Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
Learning time-series shapelets. KDD (2014)
Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme, Sergio Gutiérrez Santos
Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems. ICALT (2014)
Martin Wistuba, Lars Schmidt-Thieme
Supervised Clustering of Social Media Streams. MediaEval (2013)
Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
Time-Series Classification Through Histograms of Symbolic Polynomials. CoRR (2013)
Martin Wistuba, Lars Schmidt-Thieme
Move Prediction in Go - Modelling Feature Interactions Using Latent Factors. KI (2013)
Rasoul Karimi, Martin Wistuba, Alexandros Nanopoulos, Lars Schmidt-Thieme
Factorized Decision Trees for Active Learning in Recommender Systems. ICTAI (2013)
Martin Wistuba, Lars Schaefers, Marco Platzner
Comparison of Bayesian move prediction systems for Computer Go. CIG (2012)
André Diekwisch, Andreas Martens, Boris Wolf, Claudia Schumacher, Claudius Jähn, Daniel Mex, David Maicher, Dominik Leibenger, Gennadij Liske, Jürgen Tessmann, Kathrin Bujna, Martin Wistuba, Matthias Fischer, Matthias Strotmeier, Peter Mahlmann, Philipp Brandes
PeerGame: Ein Peer-to-Peer-basiertes Multiplayer-Echtzeit-Strategiespiel. Informatiktage (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Martin Wistuba